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31,377 Article Results

Robust resource allocation in multi-cell UE-specific RIS-assisted D2D relay networks under imperfect CSI

10.11591/ijece.v16i5.pp2575-2594
Kayode Popoola , Ayodeji Ajani , Stuart Nicholson , Muheeb Ahmed , Srilatha Narayangari Pamuri , Ibrahim Bala Alhassan
Device-to-device (D2D) communication enhances spectral efficiency but remains constrained by limited transmission range, underlay interference, and the half-duplex overhead of conventional relays. User equipment-specific reconfigurable intelligent surfaces (UE-RIS) offer a promising alternative by enabling passive beamforming to strengthen D2D links without additional spectrum consumption. However, existing studies typically assume perfect channel state information (CSI) and single-cell operation, limiting their applicability to practical deployments. This paper proposes a robust multi-cell resource allocation (RMRA) framework for UE-RIS-assisted D2D relay networks under imperfect CSI. A hybrid uncertainty model is adopted, combining statistical Gauss-Markov CSI errors for intra-cell links with bounded norm-ball uncertainty for inter-cell links. The joint optimisation of resource reuse, transmit power allocation, and RIS phase configuration is formulated as a stochastic mixed-integer nonlinear program that maximises network spectral efficiency while satisfying outage and quality-of-service constraints. To efficiently solve the problem, a three-stage algorithm is proposed comprising distance-pruned Hungarian assignment, robust power control using Bernstein-type inequality and S-procedure based semidefinite programming, and soft actor-critic (SAC) based passive beamforming. Simulation results show that RMRA achieves a 94% D2D access rate at light load and over 75% at full load, improves sum spectral efficiency by 34.7% and 70.2% over AF relaying and direct D2D, respectively, attains 118.5 bits/s/Hz/W energy efficiency, and maintains 30.2 bits/s/Hz under severe CSI uncertainty.
Volume: 16
Issue: 5
Page: 2575-2594
Publish at: 2026-10-01

CLARA: a unified hybrid ai-human receptionist framework using WebRTC and Monorepo architecture

10.11591/ijece.v16i5.pp2769-2781
Dhanush Sridhar Babu , Nagashree Nagesh , Amble Nagendra Aashuthosh , Adithya Nimmala Chandra , Muthusamy Naveen Kumar
The rapid digitalization of enterprise infrastructure has caused a major change in institutional communication. This change requires the traditional front desk to transform into a high-tech center for security and logistics. However, current automated solutions often create what is known as the “automation paradox.” In this situation, removing human oversight increases the importance and complexity of the remaining interactions. This work outlines the design, implementation, and evaluation of conversational low-latency AI receptionist agent (CLARA). CLARA is a new hybrid system which solves the problems of unattended kiosks by combining a conversational AI, which uses the Google Gemini API, with a low-latency, real-time WebRTC video calling system. The design features a strong 3-Tier Monorepo built with React, TypeScript, Express.js, Socket.IO, and PostgreSQL. This setup ensures type safety and consistency in the code across the entire stack. Key contributions of this project include a custom Socket.IO signaling server for secure peer-to-peer connections, a “Human-in-the-Loop” workflow that reduces handoff issues, and an “Offline-First” strategy ensuring the system keeps working during network disruptions. Performance tests show that CLARA achieves less than 200 ms glass-to-glass latency and handles high traffic well, proving it is an effective and reliable solution for modern visitor management.
Volume: 16
Issue: 5
Page: 2769-2781
Publish at: 2026-10-01

Temporal deep representation learning based remaining useful lifecyle prediction of electric vehicle lithium-ion batteries

10.11591/ijece.v16i5.pp2365-2378
Pradish Vaidya S. , Kevin Denzil V. , Monish S. , S. Cloudin , S. Saradha
In recent years, rapid growth of renewable energy systems and electric vehicle technologies has intensified the focus on lithium-ion batteries as critical energy storage components. Consequently, evaluating their performance and lifespan has become a significant concern in both scientific research and industrial applications. The accurate remaining useful life (RUL) prediction of lithium-ion batteries are major importance to increase energy management efficacy and increase the battery lifetime. Recently, the quick advancement of machine learning (ML) and artificial intelligence (AI) knowledge, data-driven approaches for forecasting the lithium-ion battery duration are involved extensive attention. Numerous researchers are employing deep learning (DL) approaches to make several techniques for predicting RUL, namely recurrent neural network (RNN) and convolutional neural network (CNN). This paper introduces a temporal deep representation learning based remaining useful Lifecyle prediction (TRDL-RULP) approach for electric vehicle lithium-ion batteries. The main goal of the TRDL-RULP framework is to support intelligent battery management methods and contribute to increasing the operational safety and lifecycle management of electric vehicle energy reserves systems. Initially, raw battery degradation data are processed using data normalization to eliminate scale variations and improve model stability. To enhance feature relevance and reduce dimensionality, a snake optimization-based feature selection strategy is employed to enable the extraction of the most informative degradation indicators. For RUL prediction, a long short-term memory autoencoder has been exploited to capture nonlinear temporal dependencies and extract robust latent representations. Finally, the tuna swarm optimization algorithm can be applied for optimal optimize parameters to improve convergence speed and predictive performance. The simulation study of the proposed TRDL-RULP algorithm is conducted using a benchmark lithium-ion battery degradation dataset obtained from the Kaggle repository. Extensive comparative results demonstrate the superior performance of the TRDL-RULP approach over recent methodologies.
Volume: 16
Issue: 5
Page: 2365-2378
Publish at: 2026-10-01

Development of a deep learning predictive model within a real-time business intelligence framework for goat milk supply sustainability

10.11591/ijece.v16i5.pp2641-2651
Alusyanti Primawati , Nurfidah Dwitiyanti , Ari Irawan
This study aimed to develop a multidimensional data model and a prediction model for daily goat milk production within the framework of real-time business intelligence. The model was expanded to include predictions for goat milk production in Central Java, Indonesia. The Central Java daily goat milk production dataset is real data collected directly from dairy goat farms in Central Java, such as Kejar Farm in Tegal Regency, Alan’s Diary, and BK Farm in Pati Regency. The improved multidimensional data model successfully displays information hierarchically based on time, location, and farm unit dimensions through a BI dashboard. A prediction model was built by comparing the long short-term memory (LSTM) and Prophet models with several optimization scenarios. Model evaluation showed that Stacked LSTM was the best model with a 2-layer architecture. The LSTM evaluation metrics showed an RMSE value of 3.54, MAPE of 11.81%, and R2 of 0.67, while the Prophet model was unable to capture the short-term variations and non-linear fluctuations of daily milk production data. The research results confirmed that the deep learning approach is more adaptive to the characteristics of non-stationary time series data, even in limited data. The resulting model is ready to be integrated into the RTBI Framework.
Volume: 16
Issue: 5
Page: 2641-2651
Publish at: 2026-10-01

Incremental pattern mining with novelty-based pruning for dynamic analysis and adaptive e-learning recommendations

10.11591/ijece.v16i5.pp2607-2621
Emad Mahmoud Alazazi , Ahmed Sultan Alhegami
Traditional pattern mining algorithms become increasingly inefficient in dynamic e learning environments, where learner interactions accumulate continuously. When new data arrive, these algorithms typically discard previously discovered patterns and recompute everything from scratch—a costly process that this work seeks to avoid. The proposed incremental intelligent maximal frequent itemsets (IIMFIs) framework integrates three complementary mechanisms: incremental maintenance, maximal pattern extraction, and novelty based pruning. Rather than restarting computation with each new batch, IIMFIs stores and selectively updates previously discovered patterns. The novelty threshold \theta controls pruning of highly similar candidates, further reducing the search space. Experiments on the OULAD dataset split chronologically into four batches—demonstrate several advantages. First, IIMFIs compresses the pattern set considerably: at the lowest support threshold, it reduces 2,597 patterns generated by classical algorithms to only 342, achieving a compression factor of 7.6×. Second, incremental updates achieve a 4.2× speedup over full restart and run 28% faster than restarting FP Growth. Third, activating novelty pruning (θ=0.7) improves recommendation precision by 15.5% while maintaining diversity above 0.95. Notably, F1@10 scores remain identical to those obtained from full recomputation, confirming that compactness does not compromise quality. These results demonstrate that IIMFIs provides an efficient and scalable solution for adaptive e-learning environments.
Volume: 16
Issue: 5
Page: 2607-2621
Publish at: 2026-10-01

A chip level design of a multi-mode compressive sensing image sensor

10.11591/ijece.v16i5.pp2393-2404
Zahra Sepehri , Sayed Masoud Sayedi , Ehsan Yazdian
This paper presents the full chip-level design of a multi-mode CMOS vision sensor, emphasizing the detailed implementation of its circuit architecture. The proposed chip incorporates our previously developed photodiode sensing array together with the on-chip design of control circuitry. By embedding these building blocks, the chip enables pixel-level compressive sensing and supports dual operation modes, allowing the transmission of image data in both compressed and non-compressed formats. In either mode, the sensor is capable of capturing both scene images and difference images between consecutive video frames, operating at a frame rate of 40 fps. A 64*64 vision chip is implemented using TSMC 0.18um standard CMOS technology. In the normal scene image mode, with compression (N-C) and  without compression (N-nC), the structure consumes 36.99uW and 37.09uW, respectively. Meanwhile, in the difference scene image mode, with compression (D-C) and  without compression (D-nC), it consumes 38.67uW and 38.75uW, respectively.
Volume: 16
Issue: 5
Page: 2393-2404
Publish at: 2026-10-01

Impact of optimal power flow on power quality in a low voltage three-phase network: application to a real case in Lubumbashi (DR Congo)

10.11591/ijece.v16i5.pp2304-2320
David Milambo Kasumba , Guy Nkulu Wa Ngoie , Hyacinthe Tungadio Diambomba , Jean-Paul Katond Mbay , Bonaventure Banza WA Banza
Low-voltage distribution systems (LVDS) in rapidly growing Sub-Saharan African cities frequently experience severe phase imbalance, voltage deviations, and high technical losses due to overloaded and poorly balanced feeders. Despite the increasing availability of advanced optimization techniques, their application to real low-voltage networks in developing countries remains limited. This study investigates the impact of an unbalanced three-phase optimal power flow (OPF) framework on the power quality and operational performance of a real low-voltage distribution network located in Kamalondo, Lubumbashi (Democratic Republic of the Congo). The network model was parameterized using field measurements collected between September and December 2024. The optimization problem was formulated as a mixed-integer nonlinear programming (MINLP) model and solved using the interior point method implemented in Pandapower. The proposed framework simultaneously minimizes active power losses and mitigates phase imbalance while respecting voltage and thermal operating constraints. Simulation results demonstrate significant improvements in network performance. The minimum phase-to-neutral voltage increased from 198 V to 210 V, while the maximum voltage decreased from 232 V to 226 V, improving compliance with power quality standards. The maximum current phase was reduced by 15%, and total active power losses decreased by 30%. Furthermore, the voltage unbalance factor (VUF) was reduced from 8% to 3% through optimized phase allocation and power redistribution. These results demonstrate that unbalanced three-phase OPF constitutes an effective and practical solution for improving power quality, reducing technical losses, and enhancing the operational reliability of heavily loaded low-voltage networks in developing urban environments.
Volume: 16
Issue: 5
Page: 2304-2320
Publish at: 2026-10-01

A comparative analysis of hybrid FFNN-LSTM and FFNN-RNN architectures for short term electricity load forecasting

10.11591/ijece.v16i5.pp2347-2356
Temitope Akinyede , Josephine Adenike Akinyede , Paul Kehinde Olulope , Emmanuel Taiwo Fasina , Temitope Adewale Olominu
Short-term accurate forecasting of electricity demand is crucial for power-system operation and energy scheduling and the equilibrium between electricity generation and consumption. The predicting of electric power consumption is however difficult because electricity-demand profiles are non-linear and time varying. In this paper, we consider two hybrid deep learning architectures feedforward neural network long short-term memory (FFNN-LSTM) and feedforward neural network recurrent neural network (FFNN-RNN) for multi-horizon electricity-load forecasting. The architectures we proposed combine the ability of FFNN to represent data non-linearly with the sequential modelling capability of LSTM and RNN. The authors assess the models based on historical electricity-load observations from Ado-Ekiti, at three different forecasting horizons of 24 hours, 72 hours, and 168 hours. Root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) assessed predictive performance. The findings reveal that FFNN-LSTM achieves a consistently lower RMSE across the evaluated horizons, resulting in greater effectiveness to confine relatively large forecasting errors. On the other hand, the separate LSTM and RNN models achieve lower MAE and MAPE in various scenarios, suggesting stronger short-term reaction to electricity demand. The results, therefore, show a trade-off between forecast stability and sensitivity to rapid load changes. In general, the performance of hybrid architecture is more stable compared to the standalone recurrent models which are more responsive to short-term changes when we look at most of the forecasting horizons. According to the research paper, the forecasting architectures can now be selected for smart grid applications.
Volume: 16
Issue: 5
Page: 2347-2356
Publish at: 2026-10-01

BDLock: A blockchain-enabled two tier privacy-aware federated idam service platform using RBAC

10.11591/ijece.v16i5.pp2537-2548
Muhammad Shakil Pervez , Md. Nasim Adnan , Sarker Tanveer Ahmed Rumee , Moinul Islam Zaber
Centralized identity and access management (IDAM) systems suffer from sin-gle points of failure, lack of authorization transparency, and susceptibility to in-sider threats and privilege abuse. While role-based access control (RBAC) of-fers structured permission management, its enforcement through centralized pol-icy engines introduces auditability gaps unacceptable in modern distributed service delivery environments. This paper presents BDLock, a blockchain-enabled two-tier privacy-aware federated IDAM platform integrating OAuth 2.0, OpenID Connect (OIDC), and Hyperledger Fabric 2.4. The first tier validates JSON Web Tokens (JWT) issued by Keycloak against a Spring Boot resource server, the second tier enforces immutable scope-based RBAC rights on the Hyperledger Fabric ledger, ensuring every access decision is tamper-proof and auditable. Unlike prior approaches, BDLock uniquely bridges OAuth-authenticated off-chain identities to cryptographic on-chain Fabric wallet identities, satisfying all six STRIDE-modelled threats categories across both Web2 and Web3 identity models. Validated with up to 1,800 concurrent users, BDLock achieves a peak throughput of approximately 200 transactions per second using round-robin load balancing. At high concurrency, it outperforms single-peer fallback by up to 25%. Furthermore, it maintains uninterrupted access control during peer failures, eliminating the single point of failure found in all nine compared state-of-the-art systems.
Volume: 16
Issue: 5
Page: 2537-2548
Publish at: 2026-10-01

Integration of micro-grids into Nigeria’s power system: Challenges, policy perspectives and opportunities for sustainable energy access

10.11591/ijece.v16i5.pp2321-2334
Ismaheel O. Oladejo , Sunday Adeola Ajagbe , Razaaq A. Adetutu , Mathew Timothy Oluwafemi , Matthew Olusegun Adigun
Nigeria’s electricity sector faces persistent challenges, including insufficient generation capacity, weak transmission and distribution networks, frequent outages, and continued reliance on fossil-fuel-based power. These limitations constrain economic growth and highlight the need for more resilient, decentralized, and sustainable electricity solutions. This paper presents a systematic review of the integration of micro-grids (MGs) into Nigeria’s power system, focusing on reliability improvement, control strategies, renewable energy integration, and implementation barriers. The review synthesizes literature from major academic databases to examine the current state of Nigeria’s power sector, the drivers for MG deployment, global development trends, and the policy framework supporting adoption. It evaluates the role of MGs in enhancing reliability, voltage support, resilience, rural electrification, and energy access, particularly through the use of renewable energy sources, battery energy storage systems, and intelligent energy management. Key technical issues considered include islanding capability, protection coordination, reliability indices, hierarchical control structures, and both grid-connected and standalone operation within the Nigerian context. The paper’s main contribution is a comprehensive engineering-based assessment of the reliability, control, and integration requirements of MGs in Nigeria, while also identifying major policy, financial, and infrastructural constraints. Overall, the review concludes that MGs offer a practical pathway toward a more reliable, sustainable, and inclusive electricity supply system in Nigeria.
Volume: 16
Issue: 5
Page: 2321-2334
Publish at: 2026-10-01

A systematic review and conceptual framework for cloud-based ontology-driven knowledge management systems in higher education institutions

10.11591/ijece.v16i5.pp2858-2870
Muhammad Younas , Ahmad Shukri Mohd Noor
Universities nowadays rely on digital platforms for organizing and sharing their institutional courses data. However, institutional information is scattered across different learning management systems, administrative databases, and research repositories, and there is no common system connecting them. Data were extracted from the relevant major academic database Springer Link, Scopus and IEEE for the year ranging from 2015 to 2025. Initially, 248 researches were observed out of which 30 studies review by expert were selected after filtering. The studies were analyzed for the cloud technologies in system design, the ontologies for the structured representation of knowledge and the knowledge graphs for the connected data systems. 22 out of 30 studies reported measurable improvements in knowledge accessibility. However, 78% of the reviewed systems lacked scalable reasoning mechanisms to handle large datasets, and fewer than 30% provided practical implementation guidance. Our research has three contributions: (i) literature synthesis of ontology-driven systems and cloud-based knowledge management in higher education; (ii) a system architecture that integrates cloud service models (IaaS, PaaS, SaaS), SPARQL-based knowledge graphs, and semantic technologies (OWL, RDF ontologies), grounded in a deployment scenario at Jazan University, Saudi Arabia; and (iii) a step-by-step implementation framework.
Volume: 16
Issue: 5
Page: 2858-2870
Publish at: 2026-10-01

Performance and quality analysis of brain MRI image transmission over free-space optical communication systems under severe atmospheric conditions

10.11591/ijece.v16i5.pp2526-2536
Entidhar Mhawes Zghair , Seham Hashem , Ali Hammadi
Reliable transfer of brain magnetic resonance imaging (MRI) data over atmospheric free-space optical (FSO) links is a key enabler of telemedicine, yet conventional FSO studies judge link quality by communication metrics such as the bit error rate (BER) alone, which cannot guarantee the structural and contrast fidelity that diagnosis demands. This study proposes a quality-aware FSO transmission framework for brain MRI in which link performance is assessed jointly through BER, peak signal-to-noise ratio (PSNR), and the structural similarity index (SSIM). A physical-layer FSO channel is modelled in OptiSystem 20 and co-simulated with MATLAB R2023b, which performs image serialization, reconstruction, and quality analysis. Thirty axial T2-weighted slices (256×256, 8-bit) from the public IXI dataset are transmitted at 1550 nm over clear-air, rain, and fog channels at 500, 1000, and 2000 m. Adopting conservative diagnostic thresholds of PSNR ≥ 30 dB and SSIM ≥ 0.85, the link is diagnostically usable in clear air at all tested distances (PSNR = 42.1 dB, SSIM = 0.98 at 500 m) and in rain up to 2000 m (PSNR ≥ 31.2 dB), whereas fog degrades quality below the thresholds at every distance, reaching PSNR = 22.7 dB and SSIM = 0.68 at 2000 m. A concatenated forward-error-correction (FEC) scheme is then shown to restore diagnostic quality under fog up to 1000 m, extending the usable fog range, while 2000 m remains infeasible and motivates hybrid FSO/RF operation. The framework provides quantitative deployment limits for FSO-based medical image transport.
Volume: 16
Issue: 5
Page: 2526-2536
Publish at: 2026-10-01

Design science research in developing a religious chatbot based on Bulugh al-Maram

10.11591/ijece.v16i5.pp2782-2794
Aris Tjahyanto , Irmasari Hafidz , Faizal Johan Atletiko
Chatbots have recently gained significant popularity. For instance, ChatGPT has become a preferred tool for many individuals seeking instant answers without relying on human responses. This immediacy sets chatbots apart from books, which require users to search for information manually. This time-consuming process does not align with millennials' preference for convenience and efficiency. Studying hadith independently using the Bulugh al-Maram book demands considerable time and effort. The limited use of natural language processing technologies in religious chatbots restricts their ability to handle complex inquiries effectively. A chatbot capable of answering hadith-related questions could greatly assist the public in studying hadith texts by providing direct responses without extensive searching. This chatbot was designed for web browsers, utilizing deep learning as its core technology. This research led to the development of a chatbot prototype for learning hadith from Bulugh al-Maram. Built using the design science research (DSR) methodology, the prototype achieves an intent recognition rate (IRR) of 86.82%. However, its capabilities are below the BERT model, demonstrating a strong ability to accurately interpret user questions and statements.
Volume: 16
Issue: 5
Page: 2782-2794
Publish at: 2026-10-01

Cognitive cyber-physical systems: integrating intelligence, adaptation, and real-time decision-making

10.11591/ijece.v16i5.pp2265-2271
Tole Sutikno
Cognitive cyber-physical systems (CCPS) represent a critical inflection point in the evolution of intelligent engineering systems, where computation, learning, and physical interaction are no longer separated but fused within a continuous adaptive loop. This editorial positions CCPS as the intermediate layer between foundational computational intelligence and fully autonomous socio-cognitive systems, extending the broader IJECE 2026 intellectual trajectory. In contrast to conventional cyber-physical systems governed by predefined control logic, CCPS embed learning-driven intelligence directly into sensing, communication, and control processes, enabling continuous real-time adaptation in uncertain and dynamically evolving environments. Intelligence in this paradigm is realized as embodied intelligence through continuous interaction across computational and physical layers rather than remaining confined to localized algorithmic modules. Tight coupling among perception, decision-making, and actuation enables continuous refinement of internal representations through environmental feedback. Multi-scale learning mechanisms further support simultaneous fast-response adaptation and long-horizon behavioral optimization. As a result, classical feedback architectures are transformed into predictive cognitive loops capable of anticipatory, context-aware, and autonomous operation under uncertainty. CCPS therefore provide the engineering realization of embodied intelligence, establishing the foundational bridge between computational intelligence and the emergence of future socio-cognitive systems.
Volume: 16
Issue: 5
Page: 2265-2271
Publish at: 2026-10-01

PMGHWO: A particle-guided adaptive grey wolf optimizer for large-scale traveling salesman problems

10.11591/ijece.v16i5.pp2819-2835
Hanadi A. Alshawabkah , Ayman Algafaan
The traveling salesman problem (TSP) remains challenging for metaheuristic algorithms, especially as the number of cities increases. This study proposes the particle-guided adaptive grey wolf optimizer (PMGHWO), which combines grey wolf optimizer (GWO)-based leadership guidance with crossover, particle-guided perturbation, and adaptive local refinement. PMGHWO was evaluated on ten TSPLIB instances and compared with grey wolf optimizer (GWO), genetic algorithms (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA), and Harris Hawks optimization (HHO) under the same experimental conditions. The proposed method achieved shorter average tours on all tested instances, with reductions of 18.75% compared with GWO, 19.77% with HHO, 23.30% with GA, 39.08% with WOA, and 57.51% with PSO. Friedman and Wilcoxon tests confirmed significant differences between PMGHWO and the compared methods. The ablation study showed that adaptive refinement had the strongest effect on solution quality, while the influence of particle-guided perturbation and crossover varied across the benchmark instances. Although PMGHWO required more computation than the original GWO, its runtime remained competitive with several of the other methods. Overall, the results show that PMGHWO is a promising approach for TSP optimization and warrants further evaluation on larger and more complex combinatorial problems.
Volume: 16
Issue: 5
Page: 2819-2835
Publish at: 2026-10-01
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